Abstract
The use of image processing techniques and computer vision to obtain teams statistics in different sports, currently represents a new source of information very useful for season preparedness. In this work, we propose different methods to show the players position for a specific time, to perform the point counting, and to extract clips of goal situations in the match. In order to accomplish this, we use a combination of transfer learning using a pre-trained deep neural network with a database of basketball game excerpts, and video processing techniques. As a proof of concept, the method was applied to a basketball game of local teams, showing the feasibility of the proposed approach.
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Acknowledgement
The authors would like to thank the
institute and the UNL (with CAI+D 50620190100145LI), to Enzo Ferrante and Eric Priemer by the collaboration in the original conference work, and Ryan Werth for providing the corpus of images to train the network.
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Eberle, G., Bourlot, J., Martínez, C., Albornoz, E.M. (2022). Automatic Extraction of Heat Maps and Goal Instances of a Basketball Game Using Video Processing. In: Pesado, P., Gil, G. (eds) Computer Science – CACIC 2021. CACIC 2021. Communications in Computer and Information Science, vol 1584. Springer, Cham. https://doi.org/10.1007/978-3-031-05903-2_7
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